土壤可蚀性研究进展与展望

Research progress and prospects on soil erodibility

土壤可蚀性(K)用于表征土壤对外部侵蚀力的敏感程度,是理解土壤侵蚀机理及构建侵蚀模型的重要指标,K因子值的精准获取和算法优化是土壤侵蚀模型完善的关键.该文从土壤可蚀性概念、评价指标、测定方法、时空变异性及预测值不确定性等五方面,总结国内外相关研究进展并提出展望.总体而言,小流域尺度的土壤可蚀性研究取得了丰富成果,为土壤侵蚀建模和水土保持工作实践提供了有力支持.后续应研究如何将基于非标准小区所得K值转化为标准小区条件下的数据,以形成统一的K值数据库,为适宜于大尺度区域侵蚀模型构建提供支撑;应加强示踪、遥感、数字制图技术与土壤可蚀性研究的有机结合,重点突破核素背景值测定、植被因素干扰等瓶颈;深入推进基于深度学习及数字制图的土壤可蚀性定量研究,实现新方法新技术在土壤可蚀性领域的应用.另外,受人类活动和气候变化影响,土壤可蚀性空间变异及预测值不确定性研究亦需深入,以提高土壤侵蚀预报精度,为加强水土保持空间管控提供支撑.

Soil erodibility(K)is used to characterize the sensitivity of soil to external erosive forces,and is an important indicator for understanding soil erosion mechanisms and constructing erosion models.Accurate acquisition of K factor values and algorithm optimization are key to improving soil erosion models.This article summarizes the relevant research progress at home and abroad from five aspects:the concept of soil erodibility,evaluation indicators,measurement methods,spatio-temporal variability,and uncertainty of predicted values,and offers some research prospects.Overall,research on soil erodibility at the small watershed scale has achieved rich results,providing strong support for soil erosion modeling and soil and water conservation practices.Further research should be conducted on how to convert K values obtained from non-standard residential areas into data under standard residential conditions,in order to form a unified K value database and provide support for the construction of large-scale regional erosion models.We should strengthen the organic combination of tracing,remote sensing,digital mapping technology and soil erodibility research,and focus on breaking through bottlenecks such as nuclide background value determination and vegetation factor interference.We should also carry out further quantitative research on soil erodibility based on deep learning and digital mapping,and realize the application of new methods and technologies in the field of soil erodibility.In addition,due to human activities and climate change,further research is needed on the spatial variability and uncertainty of soil erodibility predictions to improve the accuracy of soil erosion forecasting and provide support for strengthening spatial control of soil and water conservation.